Fig. 1: People and their preferences are different – data-driven personalization takes this into account

Imagine going into a store and finding the exact products you are visiting the store for right on the first shelf. And not just once, but with every subsequent purchase – even if you go to the store with completely new needs, wishes and requirements. You will find what you are looking for at that moment. Online personalization has exactly this effect through the use of artificial intelligence.

What is personalization?

Digital transformation is affecting all areas of life and work, and Corona is accelerating this development even further. Customers are increasingly online. Their surfing and consumer behavior is changing, as is their shopping expectations. You have less time, desire and patience to search for the information and products you want on websites or e-commerce stores. Instead, they expect their needs and requirements to be taken into account.

This is where personalization comes into play. For companies, it is a way to provide contextual messages, offers and experiences that are based on the individual profile of each visitor. Home pages, web designs or product selections can be presented in a user-specific manner and visitors can be addressed individually – in real time and depending on the user’s context, such as the time of day, the marketing channel and the device. Methods such as machine learning are a key factor here. Given the amount of data to be processed, only automatic AI algorithms can act quickly enough and deliver the desired results.

Personalization means increased sales

Two people visit an eShop for sporting goods at the same time – a first-time visitor via smartphone, female, 20 years old and living in Berlin and a 45-year-old male visitor from the Ruhr area via desktop, football fan, and already a customer. Both visitors come from the same marketing channel and are looking for t-shirts, but they see different landing pages, product selections and offers in real time that correspond to their profiles. Both intuitively find what they are looking for – without having to search.

This has a positive effect on your shopping experience and thus on visitor satisfaction. But such an investment also pays off for e-commerce providers: results and studies show that more relevant offers and content lead to an increase in the conversion rate and thus generate higher sales. In the future, only companies that understand that e-commerce and personalized content go hand in hand will be successful.

However, the right systems are needed to automate the processes involved in delivering relevant personalized experiences so that providers can differentiate themselves in the market and win customers.

Automatic clusters instead of manual target groups

If you consider the possible forms of personalization, several hundred different clusters of visitors to a website quickly come together, all of which need to be served individually and with the greatest possible chance of conversion.

The visitors are divided into segments – i.e. clusters are formed with people who have a high degree of similarity in their characteristics. These clusters go far beyond the target groups or personas that have often been created manually. Clusters are many times more delicate and flexible. They are no longer predefined manually and statically, but arise dynamically from the information known about the visitor, such as: B. gender, age, time, purchase history, geographical information, the device used, etc. All available information is therefore integrated into the cluster formation.

The machine learns along with you

In order to process this huge amount of different information, data sources and data sets in milliseconds and to display individual websites on the basis of this, high-performance technologies are needed that can carry out the processing automatically. The qualitative information about visitors is evaluated using various statistical methods, such as rankings, correlation analyzes or probability densities, which can be summarized under the term machine learning.

The condition: The system used must be capable of learning. The machine learning algorithm must constantly identify and define groups of buyers, i.e. form new clusters and continuously process the new information coming in. The system continually learns from visitors’ clicking and purchasing behavior and automatically optimizes the algorithm further using the knowledge gained from this.

The key advantage over conventional systems is that the algorithm also recognizes trends and seasonal effects and adapts decisions accordingly. Visitors are continuously analyzed and their contextual information is matched with the best-fitting cluster. Each cluster then automatically receives product suggestions, designs and content optimized for this group of buyers.

Where does the data come from?

Sufficient data is available for personalization. Information such as search words, marketing channel, device, gender or purchase history when combined allows the visitor to be accurately classified into a buyer group. More often, the problem is the evaluation and use of the data. This usually happens in different systems and areas of responsibility – and without a unifying overview. For example, there are existing purchase histories in CRM systems, results of advertising campaigns in campaign management and information about the performance of the landing page in tracking systems. However, all of this data and results are largely managed and evaluated independently and only used to optimize the channel that “caused” the data. In other words, the evaluation of a Google campaign is only used to optimize the Google campaigns; the evaluation of a sales email only in preparation for the next mailing. Correctly understood and applied personalization, on the other hand, uses the data across the board and in conjunction with one another, thereby creating significant added value.